Enhancing Quantitative and Data Science Education for Graduate Students in Biomedical Science

Substantial guidance is available on undergraduate quantitative training for biologists, including reports focused on biomedical science, but far less attention has been paid to the graduate curriculum. In this setting, we propose an innovative approach to quantitative education that goes beyond recommendations of a course or set of courses or activities. Due to the diversity of quantitative methods, it is infeasible to expect that biomedical PhD students can be exposed to more than a minority of the quantitative concepts and techniques employed in modern biology. We developed a novel prioritization approach in which we mined and analyzed quantitative concepts and skills from publications that faculty in relevant units deemed central to the scientific comprehension of their field. The analysis provides a prioritization of quantitative skills and concepts and could represent an effective method to drive curricular focus based upon program-specific faculty input for biological science programs of all types. Our results highlight the disconnect between typical undergraduate quantitative education for life science students, focused on continuous mathematics, and the concepts and skills in graphics, statistics, and discrete mathematics that arise from priorities established by biomedical science faculty.One Sentence SummaryWe developed a novel approach to prioritize quantitative concepts and methods for inclusion in a graduate biomedical science curriculum based upon approaches included in faculty-identified key publications.

FORMATION OF BASIC QUANTITATIVE SKILLS IN PRESCHOOL CHILDREN IN INDIVIDUAL LESSONS WITH NUMICON

The article reveals the problem of the importance of systematic work with the Numikon for the formation of mathematical skills in preschool children with normative development and intellectual disabilities. The personal experience of organizing individual lessons with Numikon is presented.

Improving Quantitative Abilities and Attitudes in Clinical Psychology Courses: Longitudinal Assessment of a Blended Learning Intervention

Background: Many students report negative attitudes toward research methods and statistics (RMS), and these attitudes are associated with impaired performance. Student interest in clinical psychology suggests that clinical courses may provide a promising venue for integrating RMS instruction. This approach may be particularly valuable for students from underrepresented groups in psychology. Objective: We evaluated whether integrating core RMS concepts into undergraduate clinical psychology courses using a blended learning intervention improved students’ quantitative knowledge and attitudes. Exploratory analyses assessed whether the intervention had differential efficacy for students from underrepresented groups. Method: Students completed pre- and post-course assessments of content knowledge, perceived RMS competence, implicit theories of quantitative skills, and statistics anxiety. We compared changes in student outcomes before ( n = 101) and after ( n = 91) implementing the blended learning intervention. Results: Overall, the intervention did not result in greater improvements in content knowledge, perceived RMS competence, or statistics anxiety. However, exploratory analyses suggested that the intervention was more effective for first-generation and racial/ethnic minority students. Change in endorsement of a growth-oriented mindset for quantitative skills was marginally stronger among students in courses implementing the blended learning intervention. Conclusion: These findings suggest that integrating RMS content in clinical psychology courses may confer modest benefits for students’ knowledge and attitudes toward quantitative skills, especially among students from underrepresented groups.

The Education of the Human Factors Engineer in the Age of Data Science

The continued advances in artificial intelligence and automation through machine learning applications, under the heading of data science, gives reason for pause within the educator community as we consider how to position future human factors engineers to contribute meaningfully in these projects. Do the lessons we learned and now teach regarding automation based on previous generations of technology still apply? What level of DS and ML expertise is needed for a human factors engineer to have a relevant role in the design of future automation? How do we integrate these topics into a field that often has not emphasized quantitative skills? This panel discussion brings together human factors engineers and educators at different stages of their careers to consider how curricula are being adapted to include data science and machine learning, and what the future of human factors education may look like in the coming years.

Advances in Info-Metrics

Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In a recent book on the Foundations of Info-Metrics, Golan (OUP, 2018) provides the theoretical underpinning of info-metrics and the necessary tools and building blocks for using that framework. This volume complements Golan’s book and expands on the series of studies on the classical maximum entropy and Bayesian methods published in the different proceedings started with the seminal collection of Levine and Tribus (1979) and continuing annually. The objective of this volume is to expand the study of info-metrics, and information processing, across the sciences and to further explore the basis of information-theoretic inference and its mathematical and philosophical foundations. This volume is inherently interdisciplinary and applications oriented. It contains some of the recent developments in the field, as well as many new cross-disciplinary case studies and examples. The emphasis here is on the interrelationship between information and inference where we view the word ‘inference’ in its most general meaning – capturing all types of problem solving. That includes model building, theory creation, estimation, prediction, and decision making. The volume contains nineteen chapters in seven parts. Although chapters in each part are related, each chapter is self-contained; it provides the necessary tools for using the info-metrics framework for solving the problem confronted in that chapter. This volume is designed to be accessible for researchers, graduate students, and practitioners across the disciplines, requiring only some basic quantitative skills. The multidisciplinary nature and applications provide a hands-on experience for the reader.

Overview of Epidemiological Investigations: Nurses’ Role

Emerging infectious diseases continue to disrupt the health care system in each level and are becoming progressively complicated to detect and treat successfully. Epidemiological investigations are a challenging task for the Health workers. The main purpose of this investigation is to identify the disease in the early stage and reduce the number death due to the sudden outbreak of any communicable diseases in the community. The steps in outbreak investigation should be “quick and clean.” Investigating an outbreak requires a combination of diplomacy, logical thinking, problem-solving ability, quantitative skills, and judgment. These skills improve with practice and experience.

Mathematical Abilities Development of Elementary School Children in Extracurricular Activities

The article highlights the relevance of the problem of improving the elementary school children’s mathematical education. The analysis of psychological and pedagogical literature on the research problem is performed. The author defines the psychological and pedagogical foundations for elementary school children’s quantitative skills development, and also notes the main forms and types of extracurricular activities in mathematics. The study presents after-school math program, aimed at elementary school children’s quantitative skills development.